Acerca de este Curso
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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Aprox. 9 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Los estudiantes que toman este Course son

  • Data Scientists
  • Machine Learning Engineers
  • Chief Technology Officers (CTOs)
  • Research Assistants
  • Data Engineers

Programa - Qué aprenderás en este curso

Semana
1
14 minutos para completar

Preface

2 videos (Total 14 minutos)
2 videos
The Goals of Evaluation10m
2 horas para completar

Basic Prediction and Recommendation Metrics

5 videos (Total 57 minutos), 1 lectura, 1 cuestionario
5 videos
Prediction Accuracy Metrics12m
Decision Support Metrics16m
Rank-Aware Top-N Metrics18m
Assignment Intro Video2m
1 lectura
Metric Computation Assignment Instructions10m
1 ejercicio de práctica
Basic Prediction and Recommendation Metrics Assignment42m
Semana
2
2 horas para completar

Advanced Metrics and Offline Evaluation

6 videos (Total 76 minutos), 1 lectura, 2 cuestionarios
6 videos
Additional Item and List-Based Metrics18m
Experimental Protocols13m
Unary Data Evaluation11m
Temporal Evaluation of Recommenders (Interview with Neal Lathia)12m
Programming Assignment Introduction8m
1 lectura
Evaluating Recommenders10m
2 ejercicios de práctica
Offline Evaluation and Metrics Quiz22m
Programming Assignment Quiz28m
Semana
3
1 hora para completar

Online Evaluation

4 videos (Total 66 minutos), 1 cuestionario
4 videos
Usage Logs and Analysis10m
A/B Studies (Field Experiments)11m
User-Centered Evaluation (Interview with Bart Knijnenburg)25m
1 ejercicio de práctica
Online Evaluation Quiz8m
Semana
4
1 hora para completar

Evaluation Design

3 videos (Total 31 minutos), 2 lecturas, 1 cuestionario
3 videos
Case Examples17m
Assignment Intro Video2m
2 lecturas
Intro to Assignment: Evaluation Design Cases10m
Quiz Debrief10m
1 ejercicio de práctica
Assignment: Evaluation Design Cases12m
4.3
23 revisionesChevron Right

Principales revisiones sobre Recommender Systems: Evaluation and Metrics

por LLJul 19th 2017

wonderful!!! They teach a lot what I did not expect!

Instructores

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Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University
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Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering

Acerca de Universidad de Minnesota

The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations....

Acerca de Programa especializado Sistemas de recomendación

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project....
Sistemas de recomendación

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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